ABSTRACT
As an essential part of data mining, association rules mining is quite interesting. To date, there are numerous publications on the regulations governing association and researchers examine the laws of association in depth from enhancing the algorithm to offering a fresh viewpoint. Models for refrigeration load predictions should play a key part in construction. They support, in particular, the optimum monitoring systems for enhanced energy conservation. This paper develops a prediction strategy for cooling load forecasting, the theory of sparse representation, and the least square support vector machine (LS-SVM). Feature extraction is often used to minimize the dimensionality of datasets containing thousands of characteristics that cannot be further processed. There are now several ways for the selection of text functions. To increase text categorization performances, we have introduced a new text categorization approach termed hybrid clonal selection genetic algorithm (HCSGA). The power demand predicting framework was originally developed based on a standard BP algorithm. The network model of the system was then determined by employing the influence variables of the power consumption as the cable network inputs. The BP network was repeatedly optimized utilizing the cloning selection particle swarm algorithm in its weight combinations, and the weight was optimized as the BP neural network’s start value and was carried through on a BP algorithm until the network fulfilled the training criteria. Skills and experience-based intuition reversal learning model (IILM) will be considered if the study into the interactive intelligent analysis system (IIAS) does not suffice. In this chapter, we will discuss the introduction to IFMI’s intuitionist approach of reversal mappings.
